FRN: Fusion and recalibration network for low-light image enhancement

Singh, Khushboo; Pandey, Ashutosh; Agarwal, Akshat; Agarwal, Mohit Kumar; Shankar, Aditya; Parihar, Anil Singh

doi:10.1007/s11042-023-15908-7

Cited by 2 publications

(1 citation statement)

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“…Perceptual gain suits an underexposed image. The feature fusion and recalibration module (FFRM) ( Singh et al, 2024 ) was proposed to recalibrate and merge the features to provide an enhanced output image. Intrinsic image decomposition ( Zhang and Ma, 2023 ) can be applied to the fusion of multiexposure to generate HDR images.…”

Section: Introductionmentioning

confidence: 99%

DCENet-based low-light image enhancement improved by spiking encoding and convLSTM

Wang,

Zhang

et al. 2024

Front. Neurosci.

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The direct utilization of low-light images hinders downstream visual tasks. Traditional low-light image enhancement (LLIE) methods, such as Retinex-based networks, require image pairs. A spiking-coding methodology called intensity-to-latency has been used to gradually acquire the structural characteristics of an image. convLSTM has been used to connect the features. This study introduces a simplified DCENet to achieve unsupervised LLIE as well as the spiking coding mode of a spiking neural network. It also applies the comprehensive coding features of convLSTM to improve the subjective and objective effects of LLIE. In the ablation experiment for the proposed structure, the convLSTM structure was replaced by a convolutional neural network, and the classical CBAM attention was introduced for comparison. Five objective evaluation metrics were compared with nine LLIE methods that currently exhibit strong comprehensive performance, with PSNR, SSIM, MSE, UQI, and VIFP exceeding the second place at 4.4% (0.8%), 3.9% (17.2%), 0% (15%), 0.1% (0.2%), and 4.3% (0.9%) on the LOL and SCIE datasets. Further experiments of the user study in five non-reference datasets were conducted to subjectively evaluate the effects depicted in the images. These experiments verified the remarkable performance of the proposed method.

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Section: Introductionmentioning

confidence: 99%